Kurppa, K. | bioRxiv Science

bioRxiv2023

Transcriptional landscape of DTP-DTEP transition reveals DUSP6 as a driver of HER2 inhibitor tolerance via Neuregulin/HER3 axis

The mechanisms promoting re-growth of dormant cancer cells under continuous tyrosine kinase inhibitor (TKI) therapy are poorly understood. Here we present transcriptional profiling of HER2+ breast cancer cells treated continuously with HER2 TKI (HER2i) therapy for 9 months. The data reveals specific gene regulatory programs associated with transition from dormant drug tolerant persister cells (DTPs) to proliferating DTEP (drug tolerant expanding persister) cells and eventually long-term resistance. Focusing on yet poorly understood phosphatases as determinants of therapy tolerance, expression of dual-specificity phosphatase DUSP6 was found inhibited in DTPs, but strongly induced upon re-growth of DTEPs. DUSP6 overexpression conferred apoptosis resistance whereas its pharmacological blockade prevented DTEP development under HER2i therapy. The DUSP6-driven HER2i tolerance was mediated by activation of neuregulin-HER3 axis, and consistent with the role of HER3 in widespread therapy tolerance, DUSP6 targeting also synergized with clinically used HER2i combination therapies. In vivo, pharmacological DUSP6 targeting induced synthetic lethal effect with HER2i in independent tumor models, and its genetic targeting reduced tumor growth in orthotopic brain metastasis model. Collectively this work provides first transcriptional landscape of DTP-DTEP transition under TKI therapy, and identify DUSP6 as a novel candidate therapy target to overcome widespread HER3-driven therapy resistance.

cancer biology↗

bioRxiv2018

Clustering based approach for population level identification of condition-associated T-cell receptor β-chain CDR3 sequences

MotivationDeep immune receptor sequencing, Repseq, provides unprecedented opportunities to identify condition-associated T-cell clones, represented by T-cell receptor (TCR) CDR3 sequences. TCR profiling has potential value for increasing immunopathological understanding of various diseases, and holds considerable clinical relevance. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has so far been limited either to mostly \"public\" CDR3 sequences, which are shared across unrelated individuals, or to comparisons of CDR3 frequencies from multiple samples from the same individual. A methodology for the identification of condition-associated TCR CDR3s by population level comparison of groups of Repseq samples is currently lacking.\n\nResultsWe implemented a computational pipeline that allows population level comparison of Repseq sample groups at the level of the immune repertoire sub-units that are shared across individuals. These sub-units (or sub-repertoires) represent shared immuno-genomic features across individuals that potentially encode common signatures in the immune response to antigens. The method first performs unsupervised clustering of CDR3 sequences within each sample based on their similarity in nucleotide or amino acid subsequence frequency. Next, it finds matching clusters across samples, the immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. We applied the method on total TCR CDR3{beta} Repseq datasets of celiac disease patients in gluten exposed and unexposed conditions, as well as on public dataset of yellow fever vaccination volunteers before and after immunization. The method successfully identified condition-associated CDR3{beta} sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3{beta} sequences with previously known CDR3{beta} species relevant to celiac disease. The method also recovered significantly high numbers of previously known CDR3{beta} sequences, relevant to each condition than would be expected by chance. We conclude that immune sub-repertoires of similar immuno-genomic features, shared across unrelated individuals, encode common immunological information. Moreover, they can serve as viable units of population level immune repertoire comparison, serving as proxy for identification of condition-associated CDR3 sequences.

bioinformatics↗